CN112965527A - Unmanned aerial vehicle formation topology generation optimization method based on improved artificial bee colony algorithm - Google Patents

Unmanned aerial vehicle formation topology generation optimization method based on improved artificial bee colony algorithm Download PDF

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CN112965527A
CN112965527A CN202110186174.1A CN202110186174A CN112965527A CN 112965527 A CN112965527 A CN 112965527A CN 202110186174 A CN202110186174 A CN 202110186174A CN 112965527 A CN112965527 A CN 112965527A
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unmanned aerial
aerial vehicle
uav
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information interaction
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CN112965527B (en
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谷晓燕
陈亮
王志钢
何锋
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Beihang University
Beijing Information Science and Technology University
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Abstract

The invention discloses an unmanned aerial vehicle formation multi-target information interaction topology optimization method based on an improved artificial bee colony algorithm, wherein chain length, average network delay and average residual energy are established according to multi-target planning in unmanned aerial vehicle diamond formation; then setting a membership function of each target, and converting the membership function into a uniform deviation value; and correcting the unmanned aerial vehicle formation constructed according to the artificial bee colony algorithm by adopting a depth neighborhood search operator DSF to obtain the unmanned aerial vehicle formation generation information interaction topology capable of seeking the minimum deviation more efficiently. The invention can meet the requirement of information interaction topology generation of different unmanned aerial vehicle formations, saves the energy of the unmanned aerial vehicles, reduces the communication delay of the unmanned aerial vehicle formations, and enables the energy distribution of the formations to be more uniform. The method has important significance for the rapid generation of the information interaction topology of the unmanned aerial vehicle formation.

Description

Unmanned aerial vehicle formation topology generation optimization method based on improved artificial bee colony algorithm
Technical Field
The invention relates to a method for generating unmanned aerial vehicle formation topology, in particular to a method for optimizing topology of unmanned aerial vehicle information interaction considering communication delay and energy, which adopts an improved artificial bee colony algorithm to optimize a multi-target information interaction topology structure when unmanned aerial vehicles are in diamond formation in unmanned aerial vehicle formation generation.
Background
Formation flying means that two or more unmanned aerial vehicles are grouped or arranged to fly according to a certain formation. In formation flight, the machines must maintain a specified distance, spacing and height difference. The basic formation forms comprise a longitudinal formation, a transverse formation, a wedge formation and a diamond formation. The central problem in formation flight is to maintain a prescribed formation and to fully exploit aircraft performance. The wing plane should be allowed to correct the deviation when the long plane selects the flight state, and the wing plane should be closely matched with the long plane. In flight, the long machine sends out instructions to change the formation scheme and the relative position of each machine according to the requirements. The formation flight has high precision, high discipline and strong visual ability, and can be used for attacking, bombing, reconnaissance, air drop, searching, shielding, defense, aerial photography, examination, performance, training and the like.
The missile autonomous formation cooperative guidance control technology, 1 st edition in 9 months 2015, page 305-307, Wusentang. A drone system architecture is indicated, as shown in figure 1. A 'formation guidance computer' in the figure is used for completing formation generation and guidance, formation control and maintenance of unmanned aerial vehicle formation, and the responsibility is to optimize and form instructions for formation guidance, control and maintenance in real time according to formation optimization indexes generated by a formation decision and management system and formation requirements of the formation, and guarantee realization of collision avoidance maneuvering control and high-quality formation of nodes through member flight control.
The self-organizing unmanned aerial vehicle cluster needs to perform information interaction with other unmanned aerial vehicles when executing tasks, so that the distance and the relative position between the unmanned aerial vehicles are kept. The link for information interaction between drones is generally called information interaction topology.
Due to the distance between the drones and the communication channel, there are various communication costs in the communication link in the information interaction formed by the drone swarm. In the unmanned aerial vehicle communication cost, only a single target of chain length or average delay is considered, the generated topology may not meet the requirement of low delay, and further delay is large, and the risk of unmanned aerial vehicle collision exists. The problem that the energy distribution is uneven due to the fact that the energy distribution of the unmanned aerial vehicle cluster is not considered, partial unmanned aerial vehicle energy is exhausted in advance, and topology is changed for many times during flight is caused, and stability of the unmanned aerial vehicle cluster is reduced. Therefore, on the premise of reducing communication energy, an optimization algorithm which can reduce communication delay and enable energy distribution of unmanned aerial vehicle formation to be more uniform is designed, and the method has very important significance.
Disclosure of Invention
The invention provides a multi-target information interaction topology optimization algorithm for unmanned aerial vehicle formation, and aims to solve the technical problem that unmanned aerial vehicle information interaction topology is variable due to uneven energy distribution in the existing unmanned aerial vehicle formation process, and further collision of unmanned aerial vehicle formation is possible. The method of the invention brings the average residual energy into a construction system of the unmanned aerial vehicle information interaction topology, and establishes a multi-target system by comprehensively considering the communication chain length, the average network delay and the average residual energy. In order to measure targets of multiple dimensions with a unified standard, the method applies target planning to convert the targets of three different dimensions (communication chain length, average network delay and average residual energy) into dimensionless deviation values, and can take balanced consideration of the influence of each target on the unmanned aerial vehicle information interaction topology construction. And analyzing the topological structure characteristics according to the characteristics of the unmanned aerial vehicle formation. And a depth-based neighborhood search operator DSF is provided, so that an improved artificial bee colony algorithm is obtained. And then, solving the optimal unmanned aerial vehicle formation information interaction topology by using an artificial bee colony algorithm. Through verification, the optimal information interaction topology can be quickly found through an improved artificial bee colony algorithm. The invention can quickly find out a satisfactory solution and can adapt to the construction of various formation information interaction topologies.
The invention relates to an unmanned aerial vehicle formation topology generation optimization method based on an improved artificial bee colony algorithm.A formation guidance computer receives interaction information of all unmanned aerial vehicles in the same formation unmanned aerial vehicle cluster from a navigation computer; the method is characterized by comprising the following specific steps:
the method comprises the following steps: constructing a two-dimensional adjacent matrix of unmanned aerial vehicle formation;
the formation guidance computer constructs an edge connection graph G (UAV, MV) according to the number of the unmanned aerial vehicles in the formation information of the unmanned aerial vehicles; a bidirectional information interaction channel exists between every two unmanned aerial vehicles, the information interaction channel is called as the side of a side communicating graph, and each unmanned aerial vehicle is the vertex of the side communicating graph;
obtaining an incidence matrix relation with unmanned aerial vehicles as vertexes from a unmanned aerial vehicle formation connected graph G (UAV, MV), namely an unmanned aerial vehicle formation two-dimensional adjacency matrix, marked as LL, and LL (L)i,j]n×n；
Li,jFor the ith unmanned plane uaviWith jth unmanned plane uavjThe communication association relationship between the two;
unmanned aerial vehicle formation two-dimensional adjacency matrix LL ═ Li,j]n×nIs corresponding to a communication in an information interaction topology; if unmanned aerial vehicle uaviWith unmanned plane uavjIf communication exists, the value is assigned to 1; otherwise, if unmanned aerial vehicle uaviWith unmanned plane uavjIf no communication exists, the value is assigned to 0;
step two: constructing a chain length matrix of the unmanned aerial vehicle formation communication link;
in the invention, the length of a communication link when information interaction is carried out between every two unmanned aerial vehicles is a relative communication distance (unit: m) value;
in the invention, the formation guidance computer acquires the effective communication range of each unmanned aerial vehicle from the navigation computer, so that a chain length matrix of the unmanned aerial vehicle formation communication link is established and marked as CC, and CC is [ C ]i,j]n×n；
Ci,jIndicating the ith unmanned plane uaviWith jth unmanned plane uavjThe length of the communication link between;
n represents the total number of unmanned aerial vehicles in a formation unmanned aerial vehicle cluster;
in the present invention, Ci,j＝di,j×Li,j，di,jFor the ith unmanned plane uaviWith jth unmanned plane uavjDistance of communication between, Li,jFor unmanned aerial vehicle uaviWith unmanned plane uavjThe communication association relationship between the two;
in the present invention, the membership function of the communication link length to be calculated is denoted as μλ(Ci,j) And is and
Figure BDA0002943167300000021
Cmaxthe longest chain length of the current iteration times;
Cminthe minimum chain length of the current iteration times;
Cnewthe chain length is the chain length of the current iteration times;
step three: constructing an average network delay matrix after the unmanned aerial vehicles are formed and networked;
in the invention, the network delay (unit: ms) occurs when information interaction is carried out between every two unmanned aerial vehicles;
in the invention, the average network delay matrix after the unmanned aerial vehicles are formed and networked is recorded as DD, and the DD is [ D ═ Di,j]n×n；
Di,jIndicating the ith unmanned plane uaviWith jth unmanned plane uavjAverage network delay in between;
n represents the total number of unmanned aerial vehicles in a formation unmanned aerial vehicle cluster;
in the present invention, Di,j＝hi,j×Li,j，hi,jFor the ith unmanned plane uaviWith jth unmanned plane uavjAverage network delay between, Li,jFor unmanned aerial vehicle uaviWith unmanned plane uavjThe communication association relationship between the two;
in the present invention, the membership function of the average network delay to be calculated is denoted as uλ(Di,j) And is and
Figure BDA0002943167300000031
Dmaxthe maximum delay for the current iteration number;
Dminthe minimum delay for the current iteration number;
Dnewdelay for the current iteration number;
step four: constructing an average residual energy matrix after the unmanned aerial vehicles are formed and networked;
in the invention, the average residual energy (unit: percentage) is obtained when information interaction is carried out between every two unmanned aerial vehicles;
in the invention, the average residual energy matrix after the unmanned aerial vehicles are formed and networked is recorded as EE, and EE is [ E ═ E [i,j]n×n；
Ei,jIndicating the ith unmanned plane uaviWith jth unmanned plane uavjAverage remaining energy in between;
n represents the total number of unmanned aerial vehicles in a formation unmanned aerial vehicle cluster;
in the present invention, Ei,j＝ri,j×Li,j，ri,jFor the ith unmanned plane uaviWith jth unmanned plane uavjAverage residual energy between, Li,jFor unmanned aerial vehicle uaviWith unmanned plane uavjThe communication association relationship between the two;
in the present invention, the membership function of the average residual energy to be calculated is denoted as uλ(Ei,j) And is and
Figure BDA0002943167300000032
the maximum average residual energy is denoted as Emax(ii) a Said EmaxThe value is 1;
the minimum average residual energy is denoted Emin(ii) a Said EminThe value is 0.1; said EminAverage residual energy corresponding to the initial solution;
the current average remaining energy is recorded as Enew；
Step five: calculating the total honey source deviation;
in the present invention, the total honey source deviation value is denoted as fit (x), and
Figure BDA0002943167300000033
Figure BDA0002943167300000034
Figure BDA0002943167300000035
Figure BDA0002943167300000036
σ1a weight that is a length of the communication link;
σ2a weight that is the average network delay;
σ3is the weight of the average remaining energy;
fit (x) represents the deviation of the whole link, and the smaller the deviation is, the better the information interaction topological performance is;
in the invention, aiming at different application scenes, the weight of each target can be adjusted to realize more prominent importance to a certain target, namely, the sigma is adjusted1,σ2,σ3A value of (d);
step six: initializing parameters of an improved artificial bee colony algorithm and establishing an initial bee source;
step 61, initializing and improving each parameter of the artificial bee colony algorithm;
in the invention, the parameters for initializing the unmanned aerial vehicle formation required to operate the improved artificial bee colony algorithm are as follows:
setting the unmanned aerial vehicle formation bee colony scale, and marking as NP;
setting the maximum loop iteration number, and recording as lambdamaxInitially, the iteration number λ is 0;
unmanned aerial vehicle uav configured as a hiring bee identityHiring beeThe number of searches for food Source Limit, recorded as LILimit；
Step 62, generating unmanned aerial vehicle information interaction of a multi-branch tree structure;
in the invention, a multi-branch tree structure is adopted to carry out primary layering on the unmanned aerial vehicle formation connectivity graph G (UAV, MV) in the step one to obtain a primary unmanned aerial vehicle information interaction topology; the first unmanned aerial vehicle information interaction topology corresponds to an initial unmanned aerial vehicle food source which is recorded as LimitInitial；
Step 63, generating an unmanned aerial vehicle food source;
relay LimitInitialThen, each time the unmanned aerial vehicle formation connectivity graph G is changed (UAV, MV), there is an unmanned aerial vehicle information interaction topology, and there is only one current iteration of unmanned aerial vehicle food source Limit corresponding to the unmanned aerial vehicle information interaction topologyλ；
Step seven: adopting a depth-based domain search operator (DSF) to search near the current information interaction topology by the employed bee unmanned aerial vehicle to obtain an unmanned aerial vehicle food source;
step 71, calculating a honey source deviation value;
in the invention, according to the information interaction topological structure every time, according to the second step, the third step and the fourth step, the fifth step is executed, and the honey source deviation value fit of the current iteration of the topology is calculatedλ(x)；
Step 72, calculating a fitness value;
in the present invention, drone uav as the identity of the employed beeHiring beeAfter returning to the formation form, the unmanned aerial vehicle uav with the identity of the follower bee in the formation form is informed by swinging dance in the information interaction topology display areaFollower beeUav after sharing information and exchanging topology informationFollower beeFitness value W according to information interaction topologyλCalculating the probability of information interaction topology selection by adopting an adaptive proportion selection mechanism, and recording the probability as PλAnd is and
Figure BDA0002943167300000041
Wλthe fitness value of the information interaction topology of the current iteration is obtained;
Wmaxa maximum fitness value representing a food source;
step 73, searching the unmanned aerial vehicle food source by adopting a deep domain search operator (DSF);
unmanned aerial vehicle food source Limit for searching current iteration by adopting depth neighborhood search operator DSFλ；
Drone uav with identity of a hiring beeHiring beeAdding 1 to the current number of searches, i.e.
Figure BDA0002943167300000042
Step 74, the unmanned aerial vehicle serving as the slave bee identity selects in the searched information interaction topology by adopting a self-adaptive proportion selection strategy, becomes the unmanned aerial vehicle adopting the peak identity, and executes the DSF;
unmanned aerial vehicle uav with identity of follower beeFollower beeAccording to the probability PλSelecting corresponding information interaction topology to become unmanned aerial vehicle uav with identity of employed beeHiring beeExecuting step 71 to step 73;
drone uav with identity of a hiring beeHiring beeThe searching times are increased by 1 time;
step 75, recording the number of iterations;
unmanned aerial vehicle uav for determining identity as employed beeHiring beeNumber of consecutive searches LILimitWhether or not it is greater than the maximum number of searches
Figure BDA0002943167300000043
If not, executing the step 71 to the step 76, if yes, executing the step eight;
step eight: the unmanned aerial vehicle serving as the identity of the reconnaissance bee adopts global random search to complete the task of searching new information interaction topology;
drone uav with identity of a hiring beeHiring beeUnmanned aerial vehicle uav capable of being converted into identity scout beeInvestigation beeThen, the uavInvestigation beeSelecting an information interaction topology from the information interaction topologies of the unmanned aerial vehicle formation, and repeatedly executing the first step to the fifth step to obtain the honey source deviation value fit of the current information interaction topologyλ(x) (ii) a If the honey source deviation value fitλ(x) Deviation value fit of honey source smaller than last timeλ-1(x) If not, selecting one information interaction topology from the unmanned aerial vehicle formation information interaction topologies again until the maximum search times are reached
Figure BDA0002943167300000051
Step nine is executed;
step nine: judging the circulation termination condition and outputting the optimal information interaction topology
If the iterative calculation reaches lambdamaxThe calculation is stopped and the position of the information interaction topology, i.e. the optimal information interaction topology, is output.
The invention has the advantages that:
the invention improves a search operator, establishes a depth-based neighborhood search operator DSF according to the characteristics of formation flight of unmanned aerial vehicles, and can more quickly discover the information interaction topology of the unmanned aerial vehicles with small deviation.
Secondly, the invention comprehensively considers three communication costs, and compared with the single communication cost of distance, the communication cost can be more accurately measured by the chain length of the communication link, the average network delay and the average residual energy.
The invention can find out satisfactory information interaction topology in a short time, and can find out satisfactory information interaction topology in 0.5 second in unmanned aerial vehicle clusters of dozens of formation.
The invention can realize information interaction topology recommendation, and the unmanned aerial vehicle formation can realize wireless communication through the recommended information interaction topology, thereby avoiding the danger brought to the unmanned aerial vehicle formation flight due to excessive information interaction topology conversion times and large information interaction delay.
Drawings
Fig. 1 is a block diagram of a drone system.
Fig. 2 is a schematic diagram of a rhombus formation of unmanned aerial vehicles.
FIG. 3 is a diagram of an objective function representing a target achievement level.
Fig. 4 is a flow chart of the unmanned aerial vehicle formation topology generation based on the improved artificial bee colony algorithm.
Fig. 5 is a topological structure diagram of unmanned aerial vehicle formation information interaction obtained by the processing of the method of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
According to fig. 1, in the known rhombus formation form of the unmanned aerial vehicles, the communication distances among the unmanned aerial vehicles during the formation flight process of the unmanned aerial vehicles are provided by a navigation computer, and the communication delays comprise link delay and equipment delay and the current remaining energy of each unmanned aerial vehicle. And the formation guidance computer receives the interaction information of each unmanned aerial vehicle in the same formation unmanned aerial vehicle cluster from the navigation computer, and performs formation generation and maintenance of the unmanned aerial vehicle interaction information topology according to an improved artificial bee colony algorithm.
In the invention, the formation of unmanned aerial vehicles is diversified, and the invention is explained by a 12-frame unmanned aerial vehicle according to a rhombus formation (as shown in fig. 2). In fig. 2, the drones of the band are called longplanes and the drones that follow the longplanes to perform the mission are called bureaucratic planes.
In the present invention, a set formed by a plurality of drones is denoted as a set UAV, and UAV ═ UAV1,uav2,…,uavi,…,uavj,…,uavn}：
uav1Representing the first drone in the fleet of drones.
uav2Representing a second drone in the fleet of drones.
uaviAnd indicating the ith unmanned aerial vehicle in the formation unmanned aerial vehicle cluster.
uavjAnd indicating the jth unmanned aerial vehicle in the formation unmanned aerial vehicle cluster.
uavnRepresenting the last drone in the fleet of drones. The lower subscript n represents the total number of drones in a fleet of drones.
Uav for convenience of explanationi、uavjAnd uavnFor unmanned aerial vehicles of different identification numbers. That is to say uavi、uavjAnd uavnNot the same drone. uaviAlso called any one of the unmanned planes in the formation unmanned plane cluster; uavjAlso known as any other drone in the fleet of drones.
For example, in a diamond formation formed by 12 drones, the leader is numbered uav1The unmanned aerial vehicle (2), namely a first unmanned aerial vehicle; as wing-machines respectivelyIs that
Figure BDA0002943167300000061
A wing plane is also called a follower.
Second frame unmanned plane uav2. Third unmanned plane uav3. Fourth unmanned plane uav4. Fifth unmanned plane uav5. Sixth unmanned plane uav6. Seventh unmanned plane uav7. Eighth unmanned plane uav8. Ninth unmanned aerial vehicle uav9. Tenth unmanned plane uav10. Eleventh unmanned plane uav11. Twelfth unmanned plane uav12。
The artificial bee colony algorithm refers to research and application of a multi-target artificial bee colony algorithm and a genetic algorithm, has a better tension, and is 1 st edition in 7 months in 2013, pages 11 to 13. The information interaction topology among the unmanned aerial vehicles is used as the food source Limit in the swarm algorithm. However, changes in the topology of drone information interaction correspond to changes in food sources.
Unmanned plane uav based on artificial bee colony algorithmiAs identity of the employed bee (i.e. when the bee is used as a house-keeping agent
Figure BDA0002943167300000062
) And finishing the DSF task of depth neighborhood search.
Unmanned plane uav based on artificial bee colony algorithmjAs a non-hiring bee identity (i.e. as
Figure BDA0002943167300000063
) On the one hand, the task of searching for a new food source is completed, and on the other hand, after the task of searching for a new food source is completed once, the identity is converted into a bee employment.
Unmanned plane uav based on artificial bee colony algorithmnAs a matter of identity of the scouting bee (i.e. detecting the identity of the bee)
Figure BDA0002943167300000064
) And completing the task of searching for a new food source.
Unmanned plane uav based on artificial bee colony algorithm1As a time of following the bee (i.e. as a time of following the bee)
Figure BDA0002943167300000065
) For completing the task of transitioning to employing bees.
In the method of the present invention, as shown in fig. 5, the drone serving bee (drone of the third tier χ after ranking) is used
Figure BDA0002943167300000066
) Unmanned aerial vehicle for non-hired bees
Figure BDA0002943167300000067
Figure BDA0002943167300000068
The current food source Limit needs to be judged through the honey source deviation value fit (x)λBy calculating the membership function mu of the communication link length separatelyλ(Ci,j) Membership function u of average network delayλ(Di,j) Membership function u of average residual energyλ(Ei,j) And normalized to the bias value, is the result of the calculations required for each iteration of the method of the invention. And calculating the total deviation of multiple targets of any given unmanned aerial vehicle interactive information topology.
In the present invention, the current iteration number is denoted as λ. The iteration number before the lambda is recorded as lambda-1; i.e. the previous iteration. The iteration number after the lambda is recorded as lambda + 1; i.e. the latter iteration.
In the present invention, the current food source, is denoted as Limitλ. Food Source at λ -1 from the previous iteration, denoted Limitλ-1(i.e., previous food source). Food source at the next iteration λ +1, denoted Limitλ+1(i.e., the next food source).
The invention discloses an unmanned aerial vehicle formation topology generation optimization method based on an improved artificial bee colony algorithm, which is operated on a formation guidance computer. The formation guidance computer receives the interaction information of each unmanned aerial vehicle in the same formation unmanned aerial vehicle cluster from the navigation computer, and the specific steps of the unmanned aerial vehicle interaction information topology in the formation generation process according to the improved artificial bee colony algorithm are as follows:
the method comprises the following steps: constructing a two-dimensional adjacent matrix of unmanned aerial vehicle formation;
and the formation guidance computer constructs an edge connection graph according to the number of the unmanned aerial vehicles in the formation information of the unmanned aerial vehicles. In the invention, a bidirectional information interaction channel exists between every two unmanned aerial vehicles, the information interaction channel is called as the side of a side connection graph, and each unmanned aerial vehicle is the vertex of the side connection graph. The formation of unmanned aerial vehicles generally comprises a longitudinal formation, a transverse formation, a wedge formation and a diamond formation.
Step 11, constructing a formation connection diagram of the unmanned aerial vehicles;
the communication graph established according to the number of the unmanned aerial vehicles in the formation of the unmanned aerial vehicles is marked as G (UAV, MV), which is simply referred to as the unmanned aerial vehicle formation communication graph. UAV represents a set of drones as the vertices of a connectivity graph, and is represented in aggregate form as UAV ═ UAV1,uav2,…,uavi,…,uavj,…,uavn}; MV represents the channel of information interaction between drones as edges of the connectivity graph, i.e. the set of connected edges, expressed in set form as MV ═ v1,v2,…,vp,…,vq,…,vm}。
Obtaining an incidence matrix relation with unmanned aerial vehicles as vertexes from a unmanned aerial vehicle formation connected graph G (UAV, MV), namely an unmanned aerial vehicle formation two-dimensional adjacency matrix, marked as LL, and LL (L)i,j]n×n。
Li,jFor the ith unmanned plane uaviWith jth unmanned plane uavjThe communication association relationship between them.
The unmanned aerial vehicle set UAV (UAV) ═1,uav2,…,uavi,…,uavj,…,uavn}。
uav1Representing the first drone in the fleet of drones.
uav2Representing a second drone in the fleet of drones.
uaviAnd indicating the ith unmanned aerial vehicle in the formation unmanned aerial vehicle cluster.
uavjAnd indicating the jth unmanned aerial vehicle in the formation unmanned aerial vehicle cluster.
uavnRepresenting the last drone in the fleet of drones. The lower subscript n represents the total number of drones in a fleet of drones.
Uav for convenience of explanationi、uavjAnd uavnFor unmanned aerial vehicles of different identification numbers. That is to say uavi、uavjAnd uavnNot the same drone. uaviAlso called any one of the unmanned planes in the formation unmanned plane cluster; uavjAlso known as any other drone in the fleet of drones.
The connection edge set MV ═ { v ═ v1,v2,…,vp,…,vq,…,vm}。
v1And forming a formation for the unmanned aerial vehicle to communicate with the first edge of the graph.
v2And forming a formation for the unmanned aerial vehicle to communicate with the second edge of the graph.
vpAnd forming a p-th edge of the communicated graph for the unmanned aerial vehicle.
vqAnd forming a q-th edge of the communicated graph for the unmanned aerial vehicle.
vmAnd forming a team for the unmanned aerial vehicle to communicate the last edge of the graph. The lower subscript m represents the total number of edges in the drone formation connectivity graph.
For convenience of explanation, vp、vqAnd vmThe connecting edges between the unmanned aerial vehicles with different identification numbers. That is to say vp、vqAnd vmNot the same edge. v. ofpThe unmanned aerial vehicle formation is also called as any one edge in the communicated graph; v. ofqAlso called unmanned aerial vehicle formation connection graph any other edge.
Step 12, completing communication to obtain a two-dimensional adjacent matrix for unmanned aerial vehicle formation;
in the invention, one edge can be formed only when any two unmanned aerial vehicles realize communication completion information interaction.
In the invention, the unmanned aerial vehicle formation two-dimensional adjacency matrix LL is [ L ═ Li,j]n×nIs a topology of interaction with informationThe communication in (1) corresponds. If unmanned aerial vehicle uaviWith unmanned plane uavjIf communication exists, the value is assigned to 1; otherwise, if unmanned aerial vehicle uaviWith unmanned plane uavjIf there is no communication, the value is assigned to 0.
Step two: constructing a chain length matrix of the unmanned aerial vehicle formation communication link;
in the invention, the length of the communication link when information interaction is carried out between every two unmanned aerial vehicles is a relative communication distance (unit: m) value. By the following table:
TABLE 1 communication distance
Figure BDA0002943167300000081
Step 21, obtaining a chain length matrix of the unmanned aerial vehicle formation communication link after finishing communication;
in the invention, the formation guidance computer acquires the effective communication range of each unmanned aerial vehicle from the navigation computer, so that a chain length matrix of the unmanned aerial vehicle formation communication link is established and marked as CC, and CC is [ C ]i,j]n×n。
Ci,jIndicating the ith unmanned plane uaviWith jth unmanned plane uavjThe length of the communication link between.
n represents the total number of drones in a fleet of drones.
In the present invention, Ci,j＝di,j×Li,j，di,jFor the ith unmanned plane uaviWith jth unmanned plane uavjDistance of communication between, Li,jFor unmanned aerial vehicle uaviWith unmanned plane uavjThe communication association relationship between them.
Step 22, a membership function of the communication link length;
in the present invention, the membership function of the communication link length to be calculated is denoted as μλ(Ci,j)：
Figure BDA0002943167300000082
CmaxThe longest chain length for the current iteration number.
CminThe minimum chain length for the current iteration number.
CnewThe chain length of the current iteration number.
In the present invention, the membership function mu for the communication link lengthλ(Ci,j) The medium minimum is the chain length of the topology using a generation algorithm that simply considers chain length. The maximum is the chain length of the initial solution that starts to be randomly generated.
Step three: constructing an average network delay matrix after the unmanned aerial vehicles are formed and networked;
in the invention, the network delay (unit: ms) occurs when information interaction is carried out between every two unmanned aerial vehicles. By the following table:
TABLE 2 network latency
Figure BDA0002943167300000091
Step 31, an average network delay matrix generated by unmanned aerial vehicle formation communication;
in the invention, the average network delay matrix after the unmanned aerial vehicles are formed and networked is recorded as DD, and the DD is [ D ═ Di,j]n×n。
Di,jIndicating the ith unmanned plane uaviWith jth unmanned plane uavjAverage network delay in between.
n represents the total number of drones in a fleet of drones.
In the present invention, Di,j＝hi,j×Li,j，hi,jFor the ith unmanned plane uaviWith jth unmanned plane uavjAverage network delay between, Li,jFor unmanned aerial vehicle uaviWith unmanned plane uavjThe communication association relationship between them.
Step 32, averaging membership functions of the network delays;
in the present inventionIn the light of the above, the membership function of the average network delay to be calculated is denoted as uλ(Di,j)：
Figure BDA0002943167300000092
DmaxThe maximum delay for the current iteration number.
DminIs the minimum delay for the current number of iterations.
DnewIs the delay of the current iteration number.
In the invention, for the calculation of the minimum delay, all the unmanned child nodes are connected to the unmanned root node, the constraint is ignored, and then u is utilizedλ(Di,j) The average network delay is calculated as the minimum delay. Said DmaxThe average delay of the randomly generated initial solution is taken. Said DminAnd taking the average delay of the piloted unmanned aerial vehicle directly sent to other unmanned aerial vehicles. The mean network delay satisfying the current is denoted as Dnew。
Point a in fig. 3 corresponds to point DminCorrespondingly, point B corresponds to Dmax。
Step four: constructing an average residual energy matrix after the unmanned aerial vehicles are formed and networked;
in the invention, the average residual energy (unit: percentage) is obtained when information interaction is carried out between every two unmanned aerial vehicles.
TABLE 3 residual energy
Figure BDA0002943167300000101
Step 41, an average residual energy matrix generated by formation communication of the unmanned aerial vehicles;
in the invention, the average residual energy matrix after the unmanned aerial vehicles are formed and networked is recorded as EE, and EE is [ E ═ E [i,j]n×n。
Ei,jIndicating the ith unmanned plane uaviWith jth unmanned plane uavjBetweenThe average remaining energy of.
n represents the total number of drones in a fleet of drones.
In the present invention, Ei,j＝ri,j×Li,j，ri,jFor the ith unmanned plane uaviWith jth unmanned plane uavjAverage residual energy between, Li,jFor unmanned aerial vehicle uaviWith unmanned plane uavjThe communication association relationship between them.
Step 42, averaging the membership functions of the residual energies;
in the present invention, the membership function of the average residual energy to be calculated is denoted as uλ(Ei,j)：
Figure BDA0002943167300000102
The maximum average residual energy is denoted as Emax. Said EmaxThe value is 1.
The minimum average residual energy is denoted Emin. Said EminThe value is 0.1. Said EminCorresponding to the average residual energy of the initial solution.
The current average remaining energy is recorded as Enew。
Point a in fig. 3 corresponds to point EminCorrespondingly, point B corresponds to point Emax。
Step five: calculating the total honey source deviation;
in the present invention, the total honey source deviation value, denoted as fit (x):
Figure BDA0002943167300000103
Figure BDA0002943167300000104
Figure BDA0002943167300000105
Figure BDA0002943167300000111
σ1is a weight of the length of the communication link.
σ2Is the weight of the average network delay.
σ3Is the weight of the average remaining energy.
fit (x) represents the deviation of the whole link, and the smaller the deviation is, the better the information interaction topological performance is.
In the invention, aiming at different application scenes, the weight of each target can be adjusted to realize more prominent importance to a certain target, namely, the sigma is adjusted1,σ2,σ3The value of (c).
Step six: initializing parameters of an improved artificial bee colony algorithm and establishing an initial bee source;
in the invention, the setting of each initialized parameter is set according to a diamond formation formed by 12 unmanned aerial vehicles. And because the traditional artificial bee colony algorithm is not suitable for the changeability of the information interaction topology of the formation form, a tree structure is added on the traditional artificial bee colony algorithm, so that the technical problem that the collision of unmanned aerial vehicle formation is possibly caused because the information interaction topology of the unmanned aerial vehicle is easy to reduce due to uneven energy distribution is solved.
In the invention, an artificial bee colony algorithm with a depth neighborhood search operator DSF is added, and the artificial bee colony algorithm is called an improved artificial bee colony algorithm.
Step 61, initializing and improving each parameter of the artificial bee colony algorithm;
in the invention, the parameters for initializing the unmanned aerial vehicle formation required to operate the improved artificial bee colony algorithm are as follows:
setting the unmanned aerial vehicle formation bee colony scale, and marking as NP; for example NP-12;
setting the maximum loop iteration number, and recording as lambdamaxAnd λmax＝120 times; initially, the iteration times lambda is 0;
unmanned aerial vehicle uav configured as a hiring bee identityHiring beeThe number of searches for food Source Limit, recorded as LILimit. The maximum number of food source searches is recorded as
Figure BDA0002943167300000112
And is
Figure BDA0002943167300000113
For example, drones that are non-hiring bee identities are:
Figure BDA0002943167300000114
Figure BDA0002943167300000115
unmanned aerial vehicle uav for non-hiring bee identityNon-hiring beeThe DSF of deep neighborhood search is carried out on the food source Limit, and the number of searches is
Figure BDA0002943167300000116
In the method, the honey source deviation value fit is not searchedλ(x) Smaller topological food sources, then drone uav that is not a bee-hiring droneNon-hiring beeWill become the identity uav of the scout beeInvestigation beeA global search is performed. fitλ(x) Is the honey source deviation value of the current iteration.
Step 62, generating unmanned aerial vehicle information interaction of a multi-branch tree structure;
in the invention, a multi-branch tree structure is adopted to carry out primary layering on the unmanned aerial vehicle formation connectivity graph G (UAV, MV) in the step one to obtain a primary unmanned aerial vehicle information interaction topology; the first unmanned aerial vehicle information interaction topology corresponds to an initial unmanned aerial vehicle food source which is recorded as LimitInitial。
Step 63, generating an unmanned aerial vehicle food source;
relay LimitInitialThen, each time the unmanned aerial vehicle formation connectivity graph G is changed (UAV, MV), there is an unmanned aerial vehicle information interaction topology, and only one current unmanned aerial vehicle information interaction topology corresponds to the unmanned aerial vehicle formation connectivity graph GIterative unmanned aerial vehicle food source Limitλ。
In the invention, the unmanned aerial vehicle information interaction topology with the multi-branch tree structure is used as the food source Limit of the unmanned aerial vehicleλThese initial solutions are generated randomly in order to wait for the drone with the identity of the hiring bee and the drone with the identity of the non-hiring bee to search for honey. The initial solution must satisfy the most basic constraints. Basic constraints include that the maximum number of hops from a source node to a target node does not exceed 15, depending on the communication protocol, and that some constraints, such as certain nodes must be parent or child, may be specified, depending on the application scenario.
The multi-branch tree and the node representation of the tree refer to the data structure and the application algorithm course written by Chen Wen Bo, pages 129-130, 1 st edition 2/2001.
Step seven: adopting a depth-based domain search operator (DSF) to search near the current information interaction topology by the employed bee unmanned aerial vehicle to obtain an unmanned aerial vehicle food source;
step 71, calculating a honey source deviation value;
in the invention, according to the information interaction topological structure every time, according to the second step, the third step and the fourth step, the fifth step is executed, and the honey source deviation value fit of the current iteration of the topology is calculatedλ(x)。
Step 72, calculating a fitness value;
in the present invention, drone uav as the identity of the employed beeHiring beeAfter returning to the formation form, the unmanned aerial vehicle uav with the identity of the follower bee in the formation form is informed by swinging dance in the information interaction topology display areaFollower beeUav after sharing information and exchanging topology informationFollower beeFitness value W according to information interaction topologyλCalculating the probability of information interaction topology selection by adopting an adaptive proportion selection mechanism, and recording the probability as PλThen, there are:
Figure BDA0002943167300000121
Wλis at presentAnd (4) fitness value of the iterative information interaction topology.
WmaxRepresenting the maximum fitness value of the food source.
Step 73, searching the unmanned aerial vehicle food source by adopting a deep domain search operator (DSF);
unmanned aerial vehicle food source Limit for searching current iteration by adopting depth neighborhood search operator DSFλ。
Drone uav with identity of a hiring beeHiring beeAdding 1 to the current number of searches, i.e.
Figure BDA0002943167300000122
Step 74, the unmanned aerial vehicle serving as the slave bee identity selects in the searched information interaction topology by adopting a self-adaptive proportion selection strategy, becomes the unmanned aerial vehicle adopting the peak identity, and executes the DSF;
unmanned aerial vehicle uav with identity of follower beeFollower beeAccording to the probability PλSelecting corresponding information interaction topology to become unmanned aerial vehicle uav with identity of employed beeHiring beeStep 71 to step 73 are executed.
Drone uav with identity of a hiring beeHiring beeThe number of searches is incremented by 1.
Step 75, recording the number of iterations;
unmanned aerial vehicle uav for determining identity as employed beeHiring beeNumber of consecutive searches LILimitWhether or not it is greater than the maximum number of searches
Figure BDA0002943167300000123
If not, go to step 71 to step 76, if yes, go to step eight.
Step eight: the unmanned aerial vehicle serving as the identity of the reconnaissance bee adopts global random search to complete the task of searching new information interaction topology;
drone uav with identity of a hiring beeHiring beeUnmanned aerial vehicle uav capable of being converted into identity scout beeInvestigation beeThen, the uavInvestigation beeSelecting an information interaction topology from unmanned aerial vehicle formation information interaction topologiesAnd repeatedly executing the step one to the step five to obtain the current information interaction topology honey source deviation value fitλ(x) In that respect If the honey source deviation value fitλ(x) Deviation value fit of honey source smaller than last timeλ-1(x) If not, selecting one information interaction topology from the unmanned aerial vehicle formation information interaction topologies again until the maximum search times are reached
Figure BDA0002943167300000124
Step nine is executed.
Step nine: judging the circulation termination condition and outputting the optimal information interaction topology
If the iterative calculation reaches lambdamaxThe calculation is stopped and the position of the information interaction topology, i.e. the optimal information interaction topology, is output.
In the invention, the position of the finally output information interaction topology is also the information interaction topology with the minimum honey source deviation.
Example 1
In embodiment 1, the data information required by the rhombus formation of the unmanned aerial vehicle is developed by adopting JAVA language, MySQL is used as a database support, and the simulation environment is Intellij IDEA (version number 2020.1) compiler.
See fig. 2 for a diamond formation consisting of 12 drones, the inter-drone distance (table 1), the average network delay (table 2) and the average remaining energy (table 3).
In embodiment 1, wireless communication can be established between any two drones, and in the network topology of the present invention, the connection between two neighboring drones is a communication link, where the number 1 on the connection indicates that there is a connection, and the number 0 indicates that there is no communication link.
In embodiment 1, the distance between two adjacent drones is referred to as an inter-plane communication distance. The CC matrix represents the inter-aircraft communication distances between 12 drones. The EE matrix represents the mean remaining energy between the 12 drones. DD represents the inter-aircraft network delay for 12 drones.
The communication control mode of the unmanned aerial vehicle formation is piloting following, and in the unmanned aerial vehicle formation, one unmanned aerial vehicle sends instructions until all unmanned aerial vehicles receive the instructions.
In this embodiment 1, a method for optimizing multi-objective information interaction topology for formation of unmanned aerial vehicles based on an improved artificial bee colony algorithm is specifically configured to perform information interaction topology optimization on parameter information of inter-aircraft communication distance, inter-aircraft average remaining energy, and inter-aircraft network delay of a known formation of unmanned aerial vehicles.
The method comprises the following steps: constructing a two-dimensional adjacent matrix of unmanned aerial vehicle formation;
and the formation guidance computer constructs a unmanned aerial vehicle formation connection graph G (UAV, MV) according to the number of the unmanned aerial vehicles in the formation information of the unmanned aerial vehicles. Obtaining a communication incidence matrix relation between the unmanned aerial vehicles from an unmanned aerial vehicle formation connection graph G (UAV, MV), wherein the communication incidence matrix relation is called an unmanned aerial vehicle formation two-dimensional adjacency matrix and is marked as LL, and a table shows each numerical value in the LL matrix:
table four-adjacency matrix
Figure BDA0002943167300000131
Step two: constructing a chain length matrix of the unmanned aerial vehicle formation communication link;
the formation guidance computer acquires the effective communication range of each unmanned aerial vehicle from the navigation computer, so that a chain length matrix of the unmanned aerial vehicle formation communication link is established and recorded as CC, numerical values of the CC matrix are listed in table 1, and a membership function mu of the communication link length is combinedλ(Ci,j) Obtaining:
Cmaxthe longest chain length for the current iteration number, CmaxThe value was 6990.
CminThe shortest chain length for the current iteration number, CminThe value is 3750.
CnewChain length for the current number of iterations, CnewThe value was 6990.
Calculated to get muλ(Ci,j)＝0
In the present invention, the membership function mu for the communication link lengthλ(Ci,j) The minimum value in the range is the chain length of the topology using a generation algorithm that simply considers the chain length, and the value is 3750. The maximum is the chain length of the initial solution that starts to be randomly generated.
Step three: constructing an average network delay matrix after the unmanned aerial vehicles are formed and networked;
the formation guidance computer acquires the network delay of each unmanned aerial vehicle from the navigation computer, so that an average network delay matrix is established after the unmanned aerial vehicles are formed into a group and networked and is marked as DD, the numerical value of the DD matrix is listed in table 2, and a membership function u of the average network delay is combinedλ(Di,j) Obtaining:
Dmaxmaximum delay for the current number of iterations, DmaxThe value is 0.4683.
DminMinimum delay for the current number of iterations, DminThe value is 0.25.
DnewDelay for current number of iterations, DnewThe value is 0.4683.
Calculated uλ(Di,j)＝0；
Step four: constructing an average residual energy matrix after the unmanned aerial vehicles are formed and networked;
the formation guidance computer acquires the residual energy of each unmanned aerial vehicle from the navigation computer, so that an average residual energy matrix established after the unmanned aerial vehicles are formed into a formation network is recorded as EE, the numerical value of the EE matrix is listed in table 3, and a membership function u of the average residual energy is combinedλ(Ei,j) Obtaining:
the maximum average residual energy is denoted as Emax. Said EmaxThe value is 1.
The minimum average residual energy is denoted Emin. Said EminThe value is 0.1. Said EminCorresponding to the average residual energy of the initial solution.
The current average remaining energy is recorded as EnewSaid E isnewThe value is 0.5892.
Calculated uλ(Ei,j)＝0.5435；
Step five: calculating the total honey source deviation;
in example 1, the total honey source bias value fit (x) is 1 × 1+1 × 1+1 × 0.3950 ═ 2.4565. Sigma1,σ2,σ3All take the value of 1.
Step six: initializing parameters of an improved artificial bee colony algorithm and establishing an initial bee source;
step 61, initializing and improving each parameter of the artificial bee colony algorithm;
in the invention, the parameters for initializing the unmanned aerial vehicle formation required to operate the improved artificial bee colony algorithm are as follows:
setting the unmanned aerial vehicle formation bee colony scale, and marking as NP; for example NP-12;
setting the maximum loop iteration number, and recording as lambdamaxAnd λmax120 times; initially, the iteration times lambda is 0;
unmanned aerial vehicle uav configured as a hiring bee identityHiring beeThe number of searches for food Source Limit, recorded as LILimit. The maximum number of times is recorded as
Figure BDA0002943167300000141
And is
Figure BDA0002943167300000142
For example, drones that are non-hiring bee identities are:
Figure BDA0002943167300000143
Figure BDA0002943167300000144
unmanned aerial vehicle uav for non-hiring bee identityNon-hiring beeThe DSF of deep neighborhood search is carried out on the food source Limit, and the number of searches is
Figure BDA0002943167300000145
The honey source deviation value fit is not searchedλ(x) Smaller topological food sources, then drone uav that is not a bee-hiring droneNon-hiring beeWill become the identity uav of the scout beeInvestigation beeA global search is performed. fitλ(x) Is the honey source deviation value of the current iteration.
Step 62, generating unmanned aerial vehicle information interaction of a multi-branch tree structure;
in the invention, a multi-branch tree structure is adopted to carry out primary layering on the unmanned aerial vehicle formation connectivity graph G (UAV, MV) in the step one to obtain a primary unmanned aerial vehicle information interaction topology; the first unmanned aerial vehicle information interaction topology corresponds to an initial unmanned aerial vehicle food source which is recorded as LimitInitial，LimitInitialExpressed as a table.
Table 5 initial food source 1
Figure BDA0002943167300000151
Table 6 initial food source 2
Figure BDA0002943167300000152
Table 7 initial food source 3
Figure BDA0002943167300000153
Table 8 initial food source 4
Figure BDA0002943167300000161
Table 9 initial food source 5
Figure BDA0002943167300000162
Table 10 initial food source 6
Figure BDA0002943167300000163
Table 11 initial food source 7
Figure BDA0002943167300000171
TABLE 12 initial food Source 8
Figure BDA0002943167300000172
TABLE 13 initial food Source 9
Figure BDA0002943167300000173
TABLE 14 initial food source 10
Figure BDA0002943167300000181
TABLE 15 initial food Source 11
Figure BDA0002943167300000182
TABLE 16 initial food source 12
Figure BDA0002943167300000183
Step 63, generating an unmanned aerial vehicle food source;
relay LimitInitialThen, each time the unmanned aerial vehicle formation connectivity graph G ═ change of (UAV, MV), there is an unmanned aerial vehicle information interaction topology, from LimitInitialIn the method, an information interaction topology is extracted to form an unmanned aerial vehicle food source Limit1. Corresponding unmanned plane food source Limit with only one current iteration0，Limit0Are shown by the following table.
Table 17 initial food source for first iteration
Figure BDA0002943167300000191
Step seven: adopting a depth-based domain search operator (DSF) to search near the current information interaction topology by the employed bee unmanned aerial vehicle to obtain an unmanned aerial vehicle food source;
step 71, calculating a first honey source deviation value;
according to the initial information interaction topological structure, the step five is executed according to the steps two, three and four, the honey source deviation value of the current iteration of the topology is calculated, and the honey source deviation value of the first time is 2.4565.
Step 72, calculating a first-time fitness value;
in the present invention, drone uav as the identity of the employed beeHiring beeAfter returning to the formation form, the unmanned aerial vehicle uav with the identity of the follower bee in the formation form is informed by swinging dance in the information interaction topology display areaFollower beeUav after sharing information and exchanging topology informationFollower beeFitness value W according to information interaction topology1Calculating the probability of information interaction topology selection by adopting an adaptive proportion selection mechanism, and recording the probability as
Figure BDA0002943167300000192
W1And the fitness value of the information interaction topology of the current iteration is obtained.
WmaxRepresenting the calculated maximum fitness value in the initial food source at 2.5646
Step 73, searching the unmanned aerial vehicle food source by adopting a deep domain search operator (DSF);
unmanned aerial vehicle food source Limit for searching current iteration by adopting depth neighborhood search operator DSF1。
Divide 12 unmanned aerial vehicles into seven layers according to the rhombus formation according to the multi-branch tree structure, do respectively: there are 1 unmanned aerial vehicle in first floor alpha
Figure BDA0002943167300000193
Second layer beta has 2 frames withoutMan-machine
Figure BDA0002943167300000194
There are 2 unmanned aerial vehicles in third floor chi
Figure BDA0002943167300000195
There are 4 unmanned aerial vehicles in fourth layer gamma
Figure BDA0002943167300000196
There are 1 unmanned aerial vehicle in fifth layer theta
Figure BDA0002943167300000197
There are 1 unmanned aerial vehicle in sixth layer ζ
Figure BDA0002943167300000198
There are 1 unmanned aerial vehicle in tail layer delta
Figure BDA0002943167300000199
With 4 unmanned aerial vehicles in fourth layer gamma
Figure BDA00029431673000001910
The positions of the unmanned aerial vehicle are sequenced and exchanged to obtain the sequenced fourth layer gamma of the unmanned aerial vehicle
Figure BDA00029431673000001911
This has just regenerated unmanned aerial vehicle's information interaction topology.
Then the ordered fourth layer gamma
Figure BDA0002943167300000201
As in the fifth layer theta
Figure BDA0002943167300000202
A parent node of (a);
the positions of the unmanned aerial vehicles with other tree structures are kept unchanged; the modified unmanned aerial vehicle formation is in the form of an adjacency list, and then a critical list is converted into an adjacency matrix, so that the current unmanned aerial vehicle information interaction topology is constructed and obtained; is represented by a table as
TABLE 18 post DSF food Source Limit1
Figure BDA0002943167300000203
Drone uav with identity of a hiring beeHiring beeThe number of searches is 0+1 to 1.
Step 74, the unmanned aerial vehicle serving as the slave bee identity selects in the searched information interaction topology by adopting a self-adaptive proportion selection strategy, becomes the unmanned aerial vehicle adopting the peak identity, and executes the DSF;
unmanned aerial vehicle uav with identity of follower beeFollower beeAccording to the probability P10.9621 the corresponding information interaction topology is selected because P10.9621 < 0.97 to become a drone uav with identity of a bee hiredHiring beeStep 71 to step 73 are executed.
Step 75, calculating a second honey source deviation value;
according to the information interaction topological structure for the first time, the step five is executed according to the steps two, three and four, the honey source deviation value of the current iteration of the topology is calculated, and the honey source deviation value for the second time is 2.3247.
Step 76, calculating a second fitness value;
in the present invention, drone uav as the identity of the employed beeHiring beeAfter returning to the formation form, the unmanned aerial vehicle uav with the identity of the follower bee in the formation form is informed by swinging dance in the information interaction topology display areaFollower beeUav after sharing information and exchanging topology informationFollower beeFitness value W according to information interaction topologyλCalculating the probability of information interaction topology selection by adopting an adaptive proportion selection mechanism, and recording the probability as
Figure BDA0002943167300000204
WλAnd the fitness value of the information interaction topology of the current iteration is obtained.
WmaxRepresenting the calculated maximum of the initial food sourcesLarge fitness value of 2.5646
Step 77, searching the unmanned aerial vehicle food source by adopting a deep domain search operator (DSF);
unmanned aerial vehicle food source Limit for searching current iteration by adopting depth neighborhood search operator DSF2。
Divide 12 unmanned aerial vehicles into seven layers according to the rhombus formation according to the multi-branch tree structure, do respectively: there are 1 unmanned aerial vehicle in first floor alpha
Figure BDA0002943167300000205
There are 2 unmanned aerial vehicles in second floor beta
Figure BDA0002943167300000206
There are 2 unmanned aerial vehicles in third floor chi
Figure BDA0002943167300000207
There are 4 unmanned aerial vehicles in fourth layer gamma
Figure BDA0002943167300000208
There are 1 unmanned aerial vehicle in fifth layer theta
Figure BDA0002943167300000209
There are 1 unmanned aerial vehicle in sixth layer ζ
Figure BDA00029431673000002010
There are 1 unmanned aerial vehicle in tail layer delta
Figure BDA00029431673000002011
With 4 unmanned aerial vehicles in fourth layer gamma
Figure BDA0002943167300000211
The positions of the unmanned aerial vehicle are sequenced and exchanged to obtain the sequenced fourth layer gamma of the unmanned aerial vehicle
Figure BDA0002943167300000212
This has just regenerated unmanned aerial vehicle's information interaction topology.
Then the ordered fourth layer gamma
Figure BDA0002943167300000213
As in the fifth layer theta
Figure BDA0002943167300000214
A parent node of (a);
the positions of the unmanned aerial vehicles with other tree structures are kept unchanged; the modified unmanned aerial vehicle formation is in the form of an adjacency list, and then a critical list is converted into an adjacency matrix, so that the current unmanned aerial vehicle information interaction topology is constructed and obtained; is represented by a table as
TABLE 19 post DSF food Source Limit2
Figure BDA0002943167300000215
Drone uav with identity of a hiring beeHiring beeThe number of searches is 1+ 1-2.
Step 78, recording the number of iterations;
drone uav with identity of a hiring beeHiring beeNumber of consecutive searches LILimit2 < max number of searches
Figure BDA0002943167300000216
Steps 71 to 76 are performed.
Reference is also made to steps 71 to 76 until drone uav, identified as a bee-hiring droneHiring beeNumber of consecutive searches LILimitGreater than the maximum number of searches
Figure BDA0002943167300000217
Step eight is executed.
Step eight: the unmanned aerial vehicle serving as the identity of the reconnaissance bee adopts global random search to complete the task of searching new information interaction topology;
drone uav with identity of a hiring beeHiring beeUnmanned aerial vehicle uav capable of being converted into identity scout beeInvestigation beeThen, the uavInvestigation beeFormation letter from unmanned aerial vehicleSelecting one information interaction topology from the information interaction topologies, and representing the selected information interaction topology by a table
Table 20 food source Limit extracted by scout bees from the initial food source120
Figure BDA0002943167300000221
Repeatedly executing the step one to the step five to obtain the current information interaction topology honey source deviation value fit120(x) 2.3950. Deviation value of honey source fit120(x) Greater than last honey source deviation value fit119(x) 0.6798, and reaches the maximum number of searches
Figure BDA00029431673000002210
Step nine is executed.
Step nine: judging the circulation termination condition and outputting the optimal information interaction topology
If the iterative calculation reaches lambdamaxThe calculation is stopped and the position of the information interaction topology, i.e. the optimal information interaction topology, is output. Is represented by a table
TABLE 21 optimal information interaction topology
Uav1 Uav2 Uav3 Uav4 Uav5 Uav6 Uav7 Uav8 Uav9 Uav10 Uav11 Uav12
Uav1 0 1 0 0 0 1 1 0 0 0 0 0
Uav2 1 0 1 0 0 0 0 0 0 0 0 1
Uav 3 0 1 0 1 0 0 0 0 0 0 0 0
Uav 4 0 0 1 0 0 0 0 0 0 1 0 0
Uav 5 0 0 0 0 0 1 0 0 0 0 1 0
Uav6 1 0 0 0 1 0 0 0 0 0 0 0
Uav7 1 0 0 0 0 0 0 1 0 0 0 0
Uav 8 0 0 0 0 0 0 1 0 1 0 0 0
Uav 9 0 0 0 0 0 0 0 1 0 0 0 0
Uav 10 0 0 0 1 0 0 0 0 0 0 0 0
Uav 11 0 0 0 0 1 0 0 0 0 0 0 0
Uav 12 0 1 0 0 0 0 0 0 0 0 0 0
In the invention, the position of the finally output information interaction topology is also the information interaction topology with the minimum honey source deviation. And (3) a corresponding unmanned aerial vehicle information interaction topological graph is shown in figure 5.
In FIG. 5, 12 unmanned aerial vehicles are arranged according to a multi-branch tree structureThe machine is divided into seven layers according to the rhombus formation form, which are respectively: there are 1 unmanned aerial vehicle in first floor alpha
Figure BDA0002943167300000222
There are 2 unmanned aerial vehicles in second floor beta
Figure BDA0002943167300000223
There are 2 unmanned aerial vehicles in third floor chi
Figure BDA0002943167300000224
There are 4 unmanned aerial vehicles in fourth layer gamma
Figure BDA0002943167300000225
There are 1 unmanned aerial vehicle in fifth layer theta
Figure BDA0002943167300000226
There are 1 unmanned aerial vehicle in sixth layer ζ
Figure BDA0002943167300000227
There are 1 unmanned aerial vehicle in tail layer delta
Figure BDA0002943167300000228
Figure BDA0002943167300000229
Representing a first drone on the first floor.
Figure BDA0002943167300000231
Representing a first drone on a second level.
Figure BDA0002943167300000232
Representing a second drone located on a second level.
Figure BDA0002943167300000233
Indicating the first at the third levelAnd erecting an unmanned aerial vehicle.
Figure BDA0002943167300000234
Showing a second drone located on a third level.
Figure BDA0002943167300000235
Showing a third drone on a third level.
Figure BDA0002943167300000236
Showing a fourth drone on the third level.
Figure BDA0002943167300000237
Showing the first drone on the fourth level.
Figure BDA0002943167300000238
Showing a second drone on a fourth level.
Figure BDA0002943167300000239
Showing a third drone on a fourth level.
Figure BDA00029431673000002310
Showing a fourth drone on a fourth level.
Figure BDA00029431673000002311
Showing the first drone on the fifth level.
Figure BDA00029431673000002312
Showing the first drone on the sixth level.
Figure BDA00029431673000002313
The representation is located at the first drone of the tail floor.
If 2 unmanned aerial vehicles in the third floor x
Figure BDA00029431673000002314
The positions of the unmanned aerial vehicle are sequenced and exchanged to obtain a sequenced third layer x
Figure BDA00029431673000002315
This has just regenerated unmanned aerial vehicle's information interaction topology.
Then the third layer X after sorting
Figure BDA00029431673000002316
As in the fourth layer gamma
Figure BDA00029431673000002317
A parent node of (a);
the positions of the unmanned aerial vehicles with other tree structures are kept unchanged; the modified unmanned aerial vehicle formation is in the form of an adjacency list, and then a critical list is converted into an adjacency matrix, so that the current unmanned aerial vehicle information interaction topology is constructed and obtained.

Claims (4)

1. An unmanned aerial vehicle formation topology generation optimization method based on an improved artificial bee colony algorithm is characterized in that a formation guidance computer receives interaction information of all unmanned aerial vehicles in the same formation unmanned aerial vehicle cluster from a navigation computer; the method is characterized by comprising the following specific steps:
the method comprises the following steps: constructing a two-dimensional adjacent matrix of unmanned aerial vehicle formation;
the formation guidance computer constructs an edge connection graph G (UAV, MV) according to the number of the unmanned aerial vehicles in the formation information of the unmanned aerial vehicles; a bidirectional information interaction channel exists between every two unmanned aerial vehicles, the information interaction channel is called as the side of a side communicating graph, and each unmanned aerial vehicle is the vertex of the side communicating graph;
from unmanned aerial vehicle formation evenObtaining an incidence matrix relation of the unmanned aerial vehicle as a vertex in a general graph G (UAV, MV), namely an unmanned aerial vehicle formation two-dimensional adjacency matrix, denoted as LL, and LL (L)i,j]n×n；
Li,jFor the ith unmanned plane uaviWith jth unmanned plane uavjThe communication association relationship between the two;
unmanned aerial vehicle formation two-dimensional adjacency matrix LL ═ Li,j]n×nIs corresponding to a communication in an information interaction topology; if unmanned aerial vehicle uaviWith unmanned plane uavjIf communication exists, the value is assigned to 1; otherwise, if unmanned aerial vehicle uaviWith unmanned plane uavjIf no communication exists, the value is assigned to 0;
step two: constructing a chain length matrix of the unmanned aerial vehicle formation communication link;
in the invention, the length of a communication link when information interaction is carried out between every two unmanned aerial vehicles is a relative communication distance (unit: m) value;
in the invention, the formation guidance computer acquires the effective communication range of each unmanned aerial vehicle from the navigation computer, so that a chain length matrix of the unmanned aerial vehicle formation communication link is established and marked as CC, and CC is [ C ]i,j]n×n；
Ci,jIndicating the ith unmanned plane uaviWith jth unmanned plane uavjThe length of the communication link between;
n represents the total number of unmanned aerial vehicles in a formation unmanned aerial vehicle cluster;
in the present invention, Ci,j＝di,j×Li,j，di,jFor the ith unmanned plane uaviWith jth unmanned plane uavjDistance of communication between, Li,jFor unmanned aerial vehicle uaviWith unmanned plane uavjThe communication association relationship between the two;
in the present invention, the membership function of the communication link length to be calculated is denoted as μλ(Ci,j)：
Figure FDA0002943167290000011
CmaxThe longest chain length of the current iteration times;
Cminthe minimum chain length of the current iteration times;
Cnewthe chain length is the chain length of the current iteration times;
step three: constructing an average network delay matrix after the unmanned aerial vehicles are formed and networked;
in the invention, the network delay (unit: ms) occurs when information interaction is carried out between every two unmanned aerial vehicles;
in the invention, the average network delay matrix after the unmanned aerial vehicles are formed and networked is recorded as DD, and the DD is [ D ═ Di,j]n×n；
Di,jIndicating the ith unmanned plane uaviWith jth unmanned plane uavjAverage network delay in between;
n represents the total number of unmanned aerial vehicles in a formation unmanned aerial vehicle cluster;
in the present invention, Di,j＝hi,j×Li,j，hi,jFor the ith unmanned plane uaviWith jth unmanned plane uavjAverage network delay between, Li,jFor unmanned aerial vehicle uaviWith unmanned plane uavjThe communication association relationship between the two;
in the present invention, the membership function of the average network delay to be calculated is denoted as uλ(Di,j)：
Figure FDA0002943167290000021
DmaxThe maximum delay for the current iteration number;
Dminthe minimum delay for the current iteration number;
Dnewdelay for the current iteration number;
step four: constructing an average residual energy matrix after the unmanned aerial vehicles are formed and networked;
in the invention, the average residual energy (unit: percentage) is obtained when information interaction is carried out between every two unmanned aerial vehicles;
in the invention, the average residual energy matrix after the unmanned aerial vehicles are formed and networked is recorded as EE, and EE is [ E ═ E [i,j]n×n；
Ei,jIndicating the ith unmanned plane uaviWith jth unmanned plane uavjAverage remaining energy in between;
n represents the total number of unmanned aerial vehicles in a formation unmanned aerial vehicle cluster;
in the present invention, Ei,j＝ri,j×Li,j，ri,jFor the ith unmanned plane uaviWith jth unmanned plane uavjAverage residual energy between, Li,jFor unmanned aerial vehicle uaviWith unmanned plane uavjThe communication association relationship between the two;
in the present invention, the membership function of the average residual energy to be calculated is denoted as uλ(Ei,j)：
Figure FDA0002943167290000031
The maximum average residual energy is denoted as Emax(ii) a Said EmaxThe value is 1;
the minimum average residual energy is denoted Emin(ii) a Said EminThe value is 0.1; said EminAverage residual energy corresponding to the initial solution;
the current average remaining energy is recorded as Enew；
Step five: calculating the total honey source deviation;
in the present invention, the total honey source deviation value, denoted as fit (x):
Figure FDA0002943167290000032
Figure FDA0002943167290000033
Figure FDA0002943167290000034
Figure FDA0002943167290000035
σ1a weight that is a length of the communication link;
σ2a weight that is the average network delay;
σ3is the weight of the average remaining energy;
fit (x) represents the deviation of the whole link, and the smaller the deviation is, the better the information interaction topological performance is;
in the invention, aiming at different application scenes, the weight of each target can be adjusted to realize more prominent importance to a certain target, namely, the sigma is adjusted1,σ2,σ3A value of (d);
step six: initializing parameters of an improved artificial bee colony algorithm and establishing an initial bee source;
step 61, initializing and improving each parameter of the artificial bee colony algorithm;
in the invention, the parameters for initializing the unmanned aerial vehicle formation required to operate the improved artificial bee colony algorithm are as follows:
setting the unmanned aerial vehicle formation bee colony scale, and marking as NP;
setting the maximum loop iteration number, and recording as lambdamaxInitially, the iteration number λ is 0;
unmanned aerial vehicle uav configured as a hiring bee identityHiring beeThe number of searches for food Source Limit, recorded as LILimit；
Step 62, generating unmanned aerial vehicle information interaction of a multi-branch tree structure;
in the invention, a multi-branch tree structure is adopted to carry out primary layering on the unmanned aerial vehicle formation connectivity graph G (UAV, MV) in the step one to obtain a primary unmanned aerial vehicleMachine information interaction topology; the first unmanned aerial vehicle information interaction topology corresponds to an initial unmanned aerial vehicle food source which is recorded as LimitInitial；
Step 63, generating an unmanned aerial vehicle food source;
relay LimitInitialThen, each time the unmanned aerial vehicle formation connectivity graph G is changed (UAV, MV), there is an unmanned aerial vehicle information interaction topology, and there is only one current iteration of unmanned aerial vehicle food source Limit corresponding to the unmanned aerial vehicle information interaction topologyλ；
Step seven: adopting a depth-based domain search operator (DSF) to search near the current information interaction topology by the employed bee unmanned aerial vehicle to obtain an unmanned aerial vehicle food source;
step 71, calculating a honey source deviation value;
in the invention, according to the information interaction topological structure every time, according to the second step, the third step and the fourth step, the fifth step is executed, and the honey source deviation value fit of the current iteration of the topology is calculatedλ(x)；
Step 72, calculating a fitness value;
in the present invention, drone uav as the identity of the employed beeHiring beeAfter returning to the formation form, the unmanned aerial vehicle uav with the identity of the follower bee in the formation form is informed by swinging dance in the information interaction topology display areaFollower beeUav after sharing information and exchanging topology informationFollower beeFitness value W according to information interaction topologyλCalculating the probability of information interaction topology selection by adopting an adaptive proportion selection mechanism, and recording the probability as PλThen, there are:
Figure FDA0002943167290000041
Wλthe fitness value of the information interaction topology of the current iteration is obtained;
Wmaxa maximum fitness value representing a food source;
step 73, searching the unmanned aerial vehicle food source by adopting a deep domain search operator (DSF);
using a depth neighborhood search operator DSFSearching current iteration unmanned aerial vehicle food source Limitλ；
Drone uav with identity of a hiring beeHiring beeAdding 1 to the current number of searches, i.e.
Figure FDA0002943167290000042
Step 74, the unmanned aerial vehicle serving as the slave bee identity selects in the searched information interaction topology by adopting a self-adaptive proportion selection strategy, becomes the unmanned aerial vehicle adopting the peak identity, and executes the DSF;
unmanned aerial vehicle uav with identity of follower beeFollower beeAccording to the probability PλSelecting corresponding information interaction topology to become unmanned aerial vehicle uav with identity of employed beeHiring beeExecuting step 71 to step 73;
drone uav with identity of a hiring beeHiring beeThe searching times are increased by 1 time;
step 75, recording the number of iterations;
unmanned aerial vehicle uav for determining identity as employed beeHiring beeNumber of consecutive searches LILimitWhether or not it is greater than the maximum number of searches
Figure FDA0002943167290000051
If not, executing the step 71 to the step 76, if yes, executing the step eight;
step eight: the unmanned aerial vehicle serving as the identity of the reconnaissance bee adopts global random search to complete the task of searching new information interaction topology;
drone uav with identity of a hiring beeHiring beeUnmanned aerial vehicle uav capable of being converted into identity scout beeInvestigation beeThen, the uavInvestigation beeSelecting an information interaction topology from the information interaction topologies of the unmanned aerial vehicle formation, and repeatedly executing the first step to the fifth step to obtain the honey source deviation value fit of the current information interaction topologyλ(x) (ii) a If the honey source deviation value fitλ(x) Deviation value fit of honey source smaller than last timeλ-1(x) If the information interaction topology is not the same as the information interaction topology, the last information interaction topology is replaced, otherwise, one information is selected from the unmanned aerial vehicle formation information interaction topology againInteracting the topology until the maximum number of searches is reached
Figure FDA0002943167290000052
Step nine is executed;
step nine: judging the circulation termination condition and outputting the optimal information interaction topology
If the iterative calculation reaches lambdamaxThe calculation is stopped and the position of the information interaction topology, i.e. the optimal information interaction topology, is output.
2. The unmanned aerial vehicle formation topology generation optimization method based on the improved artificial bee colony algorithm according to claim 1, characterized in that: the formation of the unmanned aerial vehicles comprises a longitudinal formation, a transverse formation, a wedge formation or a diamond formation.
3. The unmanned aerial vehicle formation topology generation optimization method based on the improved artificial bee colony algorithm according to claim 1, characterized in that: maximum number of loop iterations λmaxSet to 120 times.
4. The unmanned aerial vehicle formation topology generation optimization method based on the improved artificial bee colony algorithm according to claim 1, characterized in that: maximum number of food source searches
Figure FDA0002943167290000053
Set to 50 times.
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